

from typing import Any



class        A__:
						"""simple docstring"""






						def __init__(    self  ,      _lowercase    )     -> List[str]:
									a_						:						Union[str, Any] 							= data
									a_						:						Optional[int] 							= None





						def __repr__(    self    )     -> str:
									return F'''Node({self.data})'''







class        A__:
						"""simple docstring"""






						def __init__(    self    )     -> List[Any]:
									a_						:						List[str] 							= None





						def __iter__(    self    )     -> Any:
									a_						:						Union[str, Any] 							= self.head
									while node:
												yield node.data
												a_						:						Tuple 							= node.next





						def __len__(    self    )     -> int:
									return sum(1 for _ in self    )





						def __repr__(    self    )     -> str:
									return "->".join([str(_lowercase    ) for item in self]    )





						def __getitem__(    self  ,      _lowercase    )     -> Any:
									if not 0 <= index < len(self    ):
												raise ValueError("""list index out of range."""    )
									for i, node in enumerate(self    ):
												if i == index:
															return node
									return None





						def __setitem__(    self  ,      _lowercase  ,      _lowercase    )     -> None:
									if not 0 <= index < len(self    ):
												raise ValueError("""list index out of range."""    )
									a_						:						Any 							= self.head
									for _ in range(_lowercase    ):
												a_						:						Dict 							= current.next
									a_						:						List[Any] 							= data





						def 			UpperCamelCase__					(    self  ,      _lowercase    )     -> None:
									self.insert_nth(len(self    )  ,      _lowercase    )





						def 			UpperCamelCase__					(    self  ,      _lowercase    )     -> None:
									self.insert_nth(0  ,      _lowercase    )





						def 			UpperCamelCase__					(    self  ,      _lowercase  ,      _lowercase    )     -> None:
									if not 0 <= index <= len(self    ):
												raise IndexError("""list index out of range"""    )
									a_						:						Dict 							= Node(_lowercase    )
									if self.head is None:
												a_						:						str 							= new_node
									elif index == 0:
												a_						:						List[str] 							= self.head  # link new_node to head
												a_						:						List[Any] 							= new_node
									else:
												a_						:						str 							= self.head
												for _ in range(index - 1    ):
															a_						:						int 							= temp.next
												a_						:						Optional[Any] 							= temp.next
												a_						:						Optional[int] 							= new_node





						def 			UpperCamelCase__					(    self    )     -> None:  # print every node data
									print(self    )





						def 			UpperCamelCase__					(    self    )     -> Any:
									return self.delete_nth(0    )





						def 			UpperCamelCase__					(    self    )     -> Any:  # delete from tail
									return self.delete_nth(len(self    ) - 1    )





						def 			UpperCamelCase__					(    self  ,      _lowercase = 0    )     -> Any:
									if not 0 <= index <= len(self    ) - 1:  # test if index is valid
												raise IndexError("""List index out of range."""    )
									a_						:						Any 							= self.head  # default first node
									if index == 0:
												a_						:						List[str] 							= self.head.next
									else:
												a_						:						Dict 							= self.head
												for _ in range(index - 1    ):
															a_						:						List[Any] 							= temp.next
												a_						:						List[Any] 							= temp.next
												a_						:						Dict 							= temp.next.next
									return delete_node.data





						def 			UpperCamelCase__					(    self    )     -> bool:
									return self.head is None





						def 			UpperCamelCase__					(    self    )     -> None:
									a_						:						Any 							= None
									a_						:						Optional[int] 							= self.head

									while current:
												# Store the current node's next node.
												a_						:						Union[str, Any] 							= current.next
												# Make the current node's next point backwards
												a_						:						Union[str, Any] 							= prev
												# Make the previous node be the current node
												a_						:						Dict 							= current
												# Make the current node the next node (to progress iteration)
												a_						:						Tuple 							= next_node
									# Return prev in order to put the head at the end
									a_						:						List[str] 							= prev







def 					_UpperCAmelCase				(						):

			'''simple docstring'''



			a_						:						Dict 							= LinkedList()
			assert linked_list.is_empty() is True
			assert str(a__) == ""

			try:
						linked_list.delete_head()
						raise AssertionError  # This should not happen.
			except IndexError:
						assert True  # This should happen.

			try:
						linked_list.delete_tail()
						raise AssertionError  # This should not happen.
			except IndexError:
						assert True  # This should happen.

			for i in range(1_0):
						assert len(a__) == i
						linked_list.insert_nth(a__  ,	i + 1)
			assert str(a__) == "->".join(str(a__) for i in range(1  ,	1_1))

			linked_list.insert_head(0)
			linked_list.insert_tail(1_1)
			assert str(a__) == "->".join(str(a__) for i in range(0  ,	1_2))

			assert linked_list.delete_head() == 0
			assert linked_list.delete_nth(9) == 1_0
			assert linked_list.delete_tail() == 1_1
			assert len(a__) == 9
			assert str(a__) == "->".join(str(a__) for i in range(1  ,	1_0))

			assert all(linked_list[i] == i + 1 for i in range(0  ,	9)) is True

			for i in range(0  ,	9):
						a_						:						Optional[Any] 							= -i
			assert all(linked_list[i] == -i for i in range(0  ,	9)) is True

			linked_list.reverse()
			assert str(a__) == "->".join(str(a__) for i in range(-8  ,	1))


def 					_UpperCAmelCase				(						):

			'''simple docstring'''



			a_						:						int 							= [
			    -9,
			    1_0_0,
			    Node(7_7_3_4_5_1_1_2),
			    """dlrow olleH""",
			    7,
			    5_5_5_5,
			    0,
			    -192.5_5555,
			    """Hello, world!""",
			    77.9,
			    Node(1_0),
			    None,
			    None,
			    12.20,
			]
			a_						:						Dict 							= LinkedList()

			for i in test_input:
						linked_list.insert_tail(a__)

			# Check if it's empty or not
			assert linked_list.is_empty() is False
			assert (
			    str(a__) == "-9->100->Node(77345112)->dlrow olleH->7->5555->0->"
			    "-192.55555->Hello, world!->77.9->Node(10)->None->None->12.2"
			)

			# Delete the head
			a_						:						int 							= linked_list.delete_head()
			assert result == -9
			assert (
			    str(a__) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
			    "Hello, world!->77.9->Node(10)->None->None->12.2"
			)

			# Delete the tail
			a_						:						int 							= linked_list.delete_tail()
			assert result == 12.2
			assert (
			    str(a__) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
			    "Hello, world!->77.9->Node(10)->None->None"
			)

			# Delete a node in specific location in linked list
			a_						:						List[str] 							= linked_list.delete_nth(1_0)
			assert result is None
			assert (
			    str(a__) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
			    "Hello, world!->77.9->Node(10)->None"
			)

			# Add a Node instance to its head
			linked_list.insert_head(Node("""Hello again, world!"""))
			assert (
			    str(a__)
			    == "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
			    "7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None"
			)

			# Add None to its tail
			linked_list.insert_tail(a__)
			assert (
			    str(a__)
			    == "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
			    "7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None->None"
			)

			# Reverse the linked list
			linked_list.reverse()
			assert (
			    str(a__)
			    == "None->None->Node(10)->77.9->Hello, world!->-192.55555->0->5555->"
			    "7->dlrow olleH->Node(77345112)->100->Node(Hello again, world!)"
			)


def 					_UpperCAmelCase				(						):

			'''simple docstring'''



			from doctest import testmod

			testmod()

			a_						:						Any 							= LinkedList()
			linked_list.insert_head(input("""Inserting 1st at head """).strip())
			linked_list.insert_head(input("""Inserting 2nd at head """).strip())
			print("""\nPrint list:""")
			linked_list.print_list()
			linked_list.insert_tail(input("""\nInserting 1st at tail """).strip())
			linked_list.insert_tail(input("""Inserting 2nd at tail """).strip())
			print("""\nPrint list:""")
			linked_list.print_list()
			print("""\nDelete head""")
			linked_list.delete_head()
			print("""Delete tail""")
			linked_list.delete_tail()
			print("""\nPrint list:""")
			linked_list.print_list()
			print("""\nReverse linked list""")
			linked_list.reverse()
			print("""\nPrint list:""")
			linked_list.print_list()
			print("""\nString representation of linked list:""")
			print(a__)
			print("""\nReading/changing Node data using indexing:""")
			print(f'''Element at Position 1: {linked_list[1]}''')
			a_						:						Union[str, Any] 							= input("""Enter New Value: """).strip()
			print("""New list:""")
			print(a__)
			print(f'''length of linked_list is : {len(a__)}''')


if __name__ == "__main__":
	main()





import argparse
import json
import os

import torch

from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken


@torch.no_grad()
def 					_UpperCAmelCase				(						a__  ,	a__  ,	a__  ,	a__  ,	a__):

			'''simple docstring'''



			with open(a__) as metadata_file:
						a_						:						Any 							= json.load(a__)
			a_						:						Dict 							= LukeConfig(use_entity_aware_attention=a__  ,	**metadata["""model_config"""])

			# Load in the weights from the checkpoint_path
			a_						:						str 							= torch.load(a__  ,	map_location="""cpu""")

			# Load the entity vocab file
			a_						:						List[str] 							= load_entity_vocab(a__)

			a_						:						int 							= RobertaTokenizer.from_pretrained(metadata["""model_config"""]["""bert_model_name"""])

			# Add special tokens to the token vocabulary for downstream tasks
			a_						:						Optional[Any] 							= AddedToken("""<ent>"""  ,	lstrip=a__  ,	rstrip=a__)
			a_						:						int 							= AddedToken("""<ent2>"""  ,	lstrip=a__  ,	rstrip=a__)
			tokenizer.add_special_tokens({"""additional_special_tokens""": [entity_token_a, entity_token_a]})
			config.vocab_size += 2

			print(f'''Saving tokenizer to {pytorch_dump_folder_path}''')
			tokenizer.save_pretrained(a__)
			with open(os.path.join(a__  ,	LukeTokenizer.vocab_files_names["""entity_vocab_file"""])  ,	"""w""") as f:
						json.dump(a__  ,	a__)

			a_						:						List[str] 							= LukeTokenizer.from_pretrained(a__)

			# Initialize the embeddings of the special tokens
			a_						:						Optional[int] 							= state_dict["""embeddings.word_embeddings.weight"""]
			a_						:						List[str] 							= word_emb[tokenizer.convert_tokens_to_ids(["""@"""])[0]].unsqueeze(0)
			a_						:						List[Any] 							= word_emb[tokenizer.convert_tokens_to_ids(["""#"""])[0]].unsqueeze(0)
			a_						:						Optional[Any] 							= torch.cat([word_emb, ent_emb, enta_emb])

			# Initialize the query layers of the entity-aware self-attention mechanism
			for layer_index in range(config.num_hidden_layers):
						for matrix_name in ["query.weight", "query.bias"]:
									a_						:						Any 							= f'''encoder.layer.{layer_index}.attention.self.'''
									a_						:						List[str] 							= state_dict[prefix + matrix_name]
									a_						:						List[Any] 							= state_dict[prefix + matrix_name]
									a_						:						Dict 							= state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
			a_						:						str 							= state_dict["""entity_embeddings.entity_embeddings.weight"""]
			a_						:						int 							= entity_emb[entity_vocab["""[MASK]"""]]

			a_						:						int 							= LukeModel(config=a__).eval()

			a_							,					a_						:						Optional[int] 							= model.load_state_dict(a__  ,	strict=a__)
			if not (len(a__) == 1 and missing_keys[0] == "embeddings.position_ids"):
						raise ValueError(f'''Missing keys {", ".join(a__)}. Expected only missing embeddings.position_ids''')
			if not (all(key.startswith("""entity_predictions""") or key.startswith("""lm_head""") for key in unexpected_keys)):
						raise ValueError(
						    """Unexpected keys"""
						    f''' {", ".join([key for key in unexpected_keys if not (key.startswith("entity_predictions") or key.startswith("lm_head"))])}''')

			# Check outputs
			a_						:						List[Any] 							= LukeTokenizer.from_pretrained(a__  ,	task="""entity_classification""")

			a_						:						str 							= (
			    """Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the"""
			    """ new world number one avoid a humiliating second- round exit at Wimbledon ."""
			)
			a_						:						str 							= (3_9, 4_2)
			a_						:						Tuple 							= tokenizer(a__  ,	entity_spans=[span]  ,	add_prefix_space=a__  ,	return_tensors="""pt""")

			a_						:						List[str] 							= model(**a__)

			# Verify word hidden states
			if model_size == "large":
						a_						:						Optional[int] 							= torch.Size((1, 4_2, 1_0_2_4))
						a_						:						List[str] 							= torch.tensor(
						    [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]])
			else:  # base
						a_						:						List[str] 							= torch.Size((1, 4_2, 7_6_8))
						a_						:						str 							= torch.tensor([[0.0037, 0.1368, -0.0091], [0.1099, 0.3329, -0.1095], [0.0765, 0.5335, 0.1179]])

			if not (outputs.last_hidden_state.shape == expected_shape):
						raise ValueError(
						    f'''Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}''')
			if not torch.allclose(outputs.last_hidden_state[0, :3, :3]  ,	a__  ,	atol=1e-4):
						raise ValueError

			# Verify entity hidden states
			if model_size == "large":
						a_						:						Dict 							= torch.Size((1, 1, 1_0_2_4))
						a_						:						int 							= torch.tensor([[0.0466, -0.0106, -0.0179]])
			else:  # base
						a_						:						Optional[Any] 							= torch.Size((1, 1, 7_6_8))
						a_						:						str 							= torch.tensor([[0.1457, 0.1044, 0.0174]])

			if not (outputs.entity_last_hidden_state.shape != expected_shape):
						raise ValueError(
						    f'''Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is'''
						    f''' {expected_shape}''')
			if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3]  ,	a__  ,	atol=1e-4):
						raise ValueError

			# Finally, save our PyTorch model and tokenizer
			print("""Saving PyTorch model to {}""".format(a__))
			model.save_pretrained(a__)


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_						:						List[str] 							= {}
			with open(a__  ,	"""r"""  ,	encoding="""utf-8""") as f:
						for index, line in enumerate(a__):
									a_							,					a_						:						List[Any] 							= line.rstrip().split("""\t""")
									a_						:						Any 							= index

			return entity_vocab


if __name__ == "__main__":
	__snake_case				:      Tuple   		=			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument("""--checkpoint_path""", type=str, help="""Path to a pytorch_model.bin file.""")
	parser.add_argument(
	    """--metadata_path""", default=None, type=str, help="""Path to a metadata.json file, defining the configuration."""
	)
	parser.add_argument(
	    """--entity_vocab_path""",
	    default=None,
	    type=str,
	    help="""Path to an entity_vocab.tsv file, containing the entity vocabulary.""",
	)
	parser.add_argument(
	    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to where to dump the output PyTorch model."""
	)
	parser.add_argument(
	    """--model_size""", default="""base""", type=str, choices=["""base""", """large"""], help="""Size of the model to be converted."""
	)
	__snake_case				:      List[str]   		=			parser.parse_args()
	convert_luke_checkpoint(
	    args.checkpoint_path,
	    args.metadata_path,
	    args.entity_vocab_path,
	    args.pytorch_dump_folder_path,
	    args.model_size,
	)


